from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-14 14:07:54.790312
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Mon, 14, Dec, 2020
Time: 14:07:58
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.5608
Nobs: 140.000 HQIC: -44.6834
Log likelihood: 1483.77 FPE: 1.82589e-20
AIC: -45.4518 Det(Omega_mle): 9.81308e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.462957 0.175324 2.641 0.008
L1.Burgenland 0.145578 0.085136 1.710 0.087
L1.Kärnten -0.234985 0.068429 -3.434 0.001
L1.Niederösterreich 0.095593 0.206928 0.462 0.644
L1.Oberösterreich 0.253552 0.170954 1.483 0.138
L1.Salzburg 0.178485 0.087948 2.029 0.042
L1.Steiermark 0.105936 0.123950 0.855 0.393
L1.Tirol 0.141907 0.081368 1.744 0.081
L1.Vorarlberg 0.003275 0.079337 0.041 0.967
L1.Wien -0.133486 0.167061 -0.799 0.424
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.584886 0.230068 2.542 0.011
L1.Burgenland 0.007042 0.111719 0.063 0.950
L1.Kärnten 0.362341 0.089795 4.035 0.000
L1.Niederösterreich 0.130915 0.271540 0.482 0.630
L1.Oberösterreich -0.211563 0.224334 -0.943 0.346
L1.Salzburg 0.195357 0.115410 1.693 0.091
L1.Steiermark 0.238936 0.162653 1.469 0.142
L1.Tirol 0.147806 0.106775 1.384 0.166
L1.Vorarlberg 0.183771 0.104110 1.765 0.078
L1.Wien -0.618004 0.219225 -2.819 0.005
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.309864 0.074518 4.158 0.000
L1.Burgenland 0.100906 0.036185 2.789 0.005
L1.Kärnten -0.023938 0.029084 -0.823 0.410
L1.Niederösterreich 0.116098 0.087951 1.320 0.187
L1.Oberösterreich 0.280786 0.072661 3.864 0.000
L1.Salzburg -0.003762 0.037381 -0.101 0.920
L1.Steiermark -0.042736 0.052683 -0.811 0.417
L1.Tirol 0.092717 0.034584 2.681 0.007
L1.Vorarlberg 0.130707 0.033721 3.876 0.000
L1.Wien 0.046825 0.071006 0.659 0.510
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.205705 0.086898 2.367 0.018
L1.Burgenland -0.006280 0.042197 -0.149 0.882
L1.Kärnten 0.020524 0.033916 0.605 0.545
L1.Niederösterreich 0.026833 0.102562 0.262 0.794
L1.Oberösterreich 0.403524 0.084732 4.762 0.000
L1.Salzburg 0.093863 0.043591 2.153 0.031
L1.Steiermark 0.195069 0.061435 3.175 0.001
L1.Tirol 0.031352 0.040330 0.777 0.437
L1.Vorarlberg 0.101311 0.039323 2.576 0.010
L1.Wien -0.071983 0.082803 -0.869 0.385
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.643794 0.184684 3.486 0.000
L1.Burgenland 0.077903 0.089681 0.869 0.385
L1.Kärnten 0.000276 0.072082 0.004 0.997
L1.Niederösterreich -0.079425 0.217974 -0.364 0.716
L1.Oberösterreich 0.123108 0.180080 0.684 0.494
L1.Salzburg 0.038452 0.092643 0.415 0.678
L1.Steiermark 0.117335 0.130567 0.899 0.369
L1.Tirol 0.225497 0.085712 2.631 0.009
L1.Vorarlberg 0.024394 0.083573 0.292 0.770
L1.Wien -0.151662 0.175979 -0.862 0.389
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.187168 0.128050 1.462 0.144
L1.Burgenland -0.034404 0.062180 -0.553 0.580
L1.Kärnten -0.011902 0.049978 -0.238 0.812
L1.Niederösterreich 0.171508 0.151132 1.135 0.256
L1.Oberösterreich 0.400926 0.124858 3.211 0.001
L1.Salzburg -0.028274 0.064234 -0.440 0.660
L1.Steiermark -0.040625 0.090528 -0.449 0.654
L1.Tirol 0.187911 0.059428 3.162 0.002
L1.Vorarlberg 0.035326 0.057945 0.610 0.542
L1.Wien 0.142910 0.122015 1.171 0.241
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.206860 0.161158 1.284 0.199
L1.Burgenland 0.082728 0.078257 1.057 0.290
L1.Kärnten -0.046411 0.062900 -0.738 0.461
L1.Niederösterreich -0.043773 0.190208 -0.230 0.818
L1.Oberösterreich -0.128172 0.157141 -0.816 0.415
L1.Salzburg 0.008607 0.080842 0.106 0.915
L1.Steiermark 0.389595 0.113935 3.419 0.001
L1.Tirol 0.521045 0.074794 6.966 0.000
L1.Vorarlberg 0.230326 0.072927 3.158 0.002
L1.Wien -0.218247 0.153563 -1.421 0.155
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.094581 0.186582 0.507 0.612
L1.Burgenland 0.031599 0.090602 0.349 0.727
L1.Kärnten -0.111850 0.072823 -1.536 0.125
L1.Niederösterreich 0.172807 0.220215 0.785 0.433
L1.Oberösterreich 0.028350 0.181932 0.156 0.876
L1.Salzburg 0.224350 0.093596 2.397 0.017
L1.Steiermark 0.166029 0.131909 1.259 0.208
L1.Tirol 0.079814 0.086593 0.922 0.357
L1.Vorarlberg 0.034516 0.084432 0.409 0.683
L1.Wien 0.290906 0.177789 1.636 0.102
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.596934 0.103141 5.788 0.000
L1.Burgenland -0.017245 0.050084 -0.344 0.731
L1.Kärnten 0.000671 0.040256 0.017 0.987
L1.Niederösterreich -0.034579 0.121733 -0.284 0.776
L1.Oberösterreich 0.279728 0.100570 2.781 0.005
L1.Salzburg 0.007749 0.051739 0.150 0.881
L1.Steiermark 0.018875 0.072918 0.259 0.796
L1.Tirol 0.074030 0.047868 1.547 0.122
L1.Vorarlberg 0.180098 0.046673 3.859 0.000
L1.Wien -0.099218 0.098280 -1.010 0.313
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.130168 -0.007758 0.182392 0.242670 0.030145 0.082621 -0.135913 0.134503
Kärnten 0.130168 1.000000 -0.035439 0.169694 0.117809 -0.166990 0.166446 0.019465 0.287040
Niederösterreich -0.007758 -0.035439 1.000000 0.240767 0.052153 0.186004 0.088288 0.036270 0.365292
Oberösterreich 0.182392 0.169694 0.240767 1.000000 0.263962 0.268842 0.074138 0.046058 0.051062
Salzburg 0.242670 0.117809 0.052153 0.263962 1.000000 0.139595 0.046541 0.074539 -0.052563
Steiermark 0.030145 -0.166990 0.186004 0.268842 0.139595 1.000000 0.091795 0.058779 -0.176545
Tirol 0.082621 0.166446 0.088288 0.074138 0.046541 0.091795 1.000000 0.127903 0.107972
Vorarlberg -0.135913 0.019465 0.036270 0.046058 0.074539 0.058779 0.127903 1.000000 0.067890
Wien 0.134503 0.287040 0.365292 0.051062 -0.052563 -0.176545 0.107972 0.067890 1.000000